Image resizing for web-based image search

ABSTRACT

Image resizing for web-based searching is described. In one implementation, a system resizes a user-selected thumbnail image into a larger version of the image that emulates the quality of a large, original image, but without downloading the original image. First, the system extracts resizing parameters when each thumbnail image is created. Then, the system creates a codebook of primitive visual elements extracted from a collection of training images. The primitive visual elements in the codebook provide universal visual parts for reconstructing images. The codebook and a resizing plug-in can be sent once to the user over a background channel. When the user selects a thumbnail image for enlargement, the system resizes the thumbnail image via interpolation and then refines the enlarged image with primitive visual elements from the codebook. The refinement creates an enlarged image that emulates the quality of the large, original image, without downloading the original image.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/113,007, filed Apr. 30, 2008, which is a continuation-in-part of U.S.patent application Ser. No. 11/851,653, filed Sep. 7, 2007 and whichclaims priority to U.S. Provisional Patent Application No. 61/042,610,filed Apr. 4, 2008, all of which are incorporated herein by reference.

BACKGROUND

Web-based image services and applications (e.g., image searching on theweb) enrich each user's experience. Web applications distinguishthemselves through the richness of their features, and many usethumbnail images (“thumbnails”) to present a collection of images on thelimited physical area of a display screen. Thumbnail images are small,icon-size versions of a larger original image and are one of the mostcommon components of web-based image searching applications, allowingusers visual control over a large number of images that visible on onepage. One main value of thumbnails is that the user can select athumbnail in order to see the corresponding original image at a largerresolution. In many cases, however, there is an undesirable delay whilethe image data of the larger resolution version downloads over anetwork, such as the Internet.

There are several conventional ways to improve the performance ofenlarging a thumbnail at the client side. A straightforward solution isto redirect the user to the web server that is hosting the originalimage or to deliver a larger version to the client through a backgroundchannel. In either case, this inevitably taxes the bandwidth and mayincrease latency. Another intuitive solution is to directly enlarge thethumbnail image itself at the client side. Specifically, there are anumber of traditional image interpolation methods that can be applied,e.g., bilinear and bi-cubic interpolation methods. However, thesemethods usually blur the discontinuities, sacrificing visual quality.

SUMMARY

Image resizing for web-based searching is described. In oneimplementation, a system resizes a user-selected thumbnail image into alarger version of the image that emulates the quality of a large,original image, but without downloading the original image. First, thesystem extracts resizing parameters when each thumbnail image iscreated. Then, the system creates a codebook of primitive visualelements extracted from a collection of training images. The primitivevisual elements in the codebook provide universal visual parts forreconstructing images. The codebook and a resizing plug-in can be sentonce to the user over a background channel. When the user selects athumbnail image for enlargement, the system resizes the thumbnail imagevia interpolation and then refines the enlarged image with primitivevisual elements from the codebook. The refinement creates an enlargedimage that emulates the quality of the large, original image, withoutdownloading the original image.

This summary is provided to introduce the subject matter of imageresizing for web-based image searching, which is further described belowin the Detailed Description. This summary is not intended to identifyessential features of the claimed subject matter, nor is it intended foruse in determining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an exemplary image-resizing system architecturefor web-based searching.

FIG. 2 is a block diagram of server-side components for thumbnailbuilding and codebook training.

FIG. 3 is a diagram of exemplary primitive patch extraction andprimitive patches.

FIG. 4 is a block diagram of exemplary patch mapping.

FIG. 5 is a block diagram of an exemplary trainer for primitive patchlearning.

FIG. 6 is a diagram of exemplary predictive coding of 9×9 blocks.

FIG. 7 is a diagram of an exemplary generalized scenario of thumbnailenlarging.

FIG. 8 is a block diagram of exemplary thumbnail enlarging components.

FIG. 9 is a block diagram of an exemplary image hallucinator andblender.

FIG. 10 is a flow diagram of an exemplary method of image resizing forweb-based image searching.

DETAILED DESCRIPTION

Overview

This disclosure describes an image resizing system for web-based imagebrowsing and searching. An exemplary system applies innovativelearning-based techniques that enable a user to enlarge a smallthumbnail image to the original size image without actually downloadingthe original image. Instead, the exemplary system achieves thisrevolutionary effect by using primitive visual elements and an exemplarycodebook to forego downloading the bulk of images at their fullresolution.

As a brief summary of the theory underlying the exemplary image resizingsystem, contents of images may vary broadly, but the primary visualelements (e.g., edge, color, shape, texture) always exist in variousnatural images. Thus, the exemplary system enlarges a small image usingthese elements learned from some training images, as building blocks.The exemplary codebook is trained from images collected from popularimage search queries, then compressed to reduce file size, and deliveredto clients through background channels. In one implementation, with thereceived codebook and an image resizing plug-in, users can directlyenlarge thumbnails of interest with automatic quality and complexitycontrol, but without having to download a larger image.

Exemplary System Architecture

Web-based image searching is described as an exemplified application forthe example image-resizing system architecture described herein. Theimage-resizing system conducts online serving of image search queriesbased on offline-built indices and thumbnails. At the server side, alarge volume of images are crawled offline from the Internet in order tobuild ranked searching indices and to generate thumbnail images storedin the serving nodes. Meanwhile, these images, which are selected frompopular queries, are fed into an image trainer to create a codebook (orits enhancement) that can be delivered offline to clients. At the clientside, users enlarge the received thumbnail images to a pre-definedresolution based on the codebook, without downloading the originalimages. The goal of the innovative training-based image resizing is toprovide a high degree of quality control for thumbnail previewing inweb-based image applications and thereby enhance the enjoyment of theuser's experience.

FIG. 1 depicts an illustrative web-based image search system 100 thatemploys training-based thumbnail image resizing. The servers include acrawler 102, a thumbnail builder 104 and a codebook generator 106 aswell as other innovative and conventional components (e.g., servingnodes 108, an index builder, and image search engine 110). Similar toconventional image search systems, the crawler 102 is used to collectimages at their original resolutions from web servers 112 throughout theInternet. These crawled images are then analyzed to extract metadata anddownsized to thumbnails. The thumbnail builder 104 can be a conventionalserver implementation but capable of some additional operations toimplement exemplary image resizing. The codebook generator 106 createsthe codebook 116, to be used for learning-based image resizing at theclient side 114. As shown in FIG. 1, the trained and compressed codebook116 can be stored at the image search engine 110 or in a front-end webserver 118, and then delivered to the end user 114 through a backgroundchannel 120 upon request. To have the best degree of user experience,the codebook 116 is trained with images corresponding to statisticallypopular image queries.

The service for image search querying can have similar operation toexisting applications, such as MICROSOFT's LIVE image search (MicrosoftCorp., Redmond, Wash.). The client-side user 114 can thus conductconventional operations such as previewing thumbnail images associatedwith search results. In addition, exemplary image-resizing enginecomponents (FIGS. 8 and 9) can be received at the user's browser as aplug-in 122. Thus, both the plug-in 122 and the codebook 116 can bedelivered to users 114 through the background channel 120. With theexemplary plug-in 122, the user 114 can enlarge thumbnail images ofinterest to a specified larger resolution, without downloading theirrespective original images. The same codebook 116 can be used to enlargethumbnail images generally. A codebook update is also supported in theexemplary image-resizing system 100, because the popularity of imagequeries varies from time to time. To save costs at both servers andclients 114, the codebook 116 adapts to new popular queries viaincremental training. Thus, only the updated part or the “delta” betweenthe former version and the updated codebook 116 is delivered to users114, e.g., through a background channel 120.

Exemplary Server-Side Components and Methods

FIG. 2 depicts exemplary server-side components 200 and showsrelationships between the crawler 102, codebook generator 106, andthumbnail builder 104. The crawler 102 conducts the conventionaloperations of crawling images from the Internet or other image sources.The crawled images 202 are then used to train the codebook 116 and buildthumbnails 204. It should be noted that it is unnecessary to store thecrawled images 202 in these servers after processing. Detailed operationof the codebook generator 106 and the thumbnail builder 104 are nowdescribed.

In the codebook generator 106, the selector 208 uses statisticallypopular queries 206 to choose crawled images 202 to become trainingimages 203 for learning. In other words, only images with highpopularity among the top queries are used for training. As introducedabove, although in general the contents of images may vary broadly, theprimary or “primitive” visual elements, such as edge, color, shape,texture, etc., exist ubiquitously among a variety of natural images. Thetraining module (“trainer”) 210 extracts these primitive visual elementsas exemplars for generic image reconstruction.

In one implementation, the trainer 210 extracts a generic set ofedge-centered N×N pixel patches (“primitive patches”) as primitivevisual elements and stores this set of image building blocks in thecodebook 116. Then the stored primitive patches can be used asgeneral-purpose, fundamental parts for rebuilding many images. As shownin FIG. 2, the exemplary system 100 also supports incremental training.When the popular queries 206 change, some newly crawled images 202 areused to update the codebook 116. Exemplary training methods will bedescribed in a section further below. The compressor 212 processes thetrained codebook 116 to save data space and transmission bandwidth.Exemplary compression techniques are also described more fully below.

In one implementation, the thumbnail builder 104 uses conventionaltechniques or a conventional thumbnail generator 216 to create thethumbnail images 204 and extracts image metadata, such as image sizeinformation, for compilation by a metadata collector 218. In addition,an “image-resizing-parameter” extracting module (“parameter extractor”)220 collects parameters 222 to improve image resizing.

A thumbnail image 204 of each original image 202 is enlarged and thenquality-refined based on the trained codebook 116, and the enlargedimage is then compared to the originally crawled image 202. If thedistortion (e.g., the mean square error, MSE) is smaller than athreshold, then in one implementation, a flag with “1” indicating“suitable for codebook-based resizing” is created; otherwise, a flagwith “0” indicating “un-suitable” is created. The flag can correspond toeither an entire image or a region. In the latter case, a collection offlags is created as metadata information 214.

Exemplary Codebook Training—Introduction

In the exemplary image-resizing system 100, the primitive visualelements can be edge-centered N×N patches, dubbed “primitive patches” asintroduced above. The edges for generating the primitive patches areextracted by convolving image signal I(x) with a derivative of aGaussian function at scale σ and orientation θ. An edge point isidentified by detecting the local maximum in the magnitude of theresponse. Then, the system extracts primal sketch regions along the edgepoints whose local maximum is recognized as an edge. After high-passfiltering, the patches containing high frequency signal content of theprimal sketch regions are treated as primitive patches.

A detailed description of the training process is provided in a sectionbelow. But in brief summary, given an input image, the trainer 210introduces a distortion module to simulate the process of lossycompression. For example, the trainer 210 can employ a down-samplefilter followed by an up-sample filter in the distortion module.Orientation-energy-based edge detection is then performed on thereconstructed distorted image. According to the detected edgeinformation, primal sketch regions are determined on both the high-passfiltered distorted image and differential signal between the originalimage and the distorted image. In this training, the trainer 210 treatsa distorted primitive patch and the differential primitive patch at thesame position as a “pair” in the following process. The differential isthe delta that can be added to restore the distorted image back to nearthe quality of the original image. After normalization, a pair ofprimitive patches is categorized into several categories by an edgeclassifier according to the edge type and orientation of the distortedprimitive patch, and stored into the training set correspondingly.Consequently, certain clustering approaches may be applied to shrink thesize of training set to a desired level. This process, along with somebackground, is now presented in detail.

Detailed Description of Codebook Training

The concept of primitive visual elements has been discussed extensivelyin the literature of computer vision. A primitive visual element is agraphics element used as a building block for creating images. Imageprimitive elements, which aim for direct insertion into the visualattributes of an image, consist of individual graphic entities. Such aprimitive element can be a line, a vector, a texture, or other visualfeature. Within the scope of visual features, each feature can befurther classified as a general feature (e.g., color, texture, shape,etc.) or a domain-specific feature that is application-dependent, suchas directed for rendering human faces. In fact, some of the primitivefeatures have been utilized in image compression that is based onstatistical principles, for example, compression by vector quantization,matching pursuit, edge-based schemes, etc.

In one implementation, the exemplary image resizing system 100 uses thegeneral primitives that are retrieved by visual pattern analysis andrepresented by image exemplars. On one hand, primitive elements, such aslines, junctions, edges, and so forth, are robust in terms of perceptualquality. On the other hand, not all primitive elements can bewell-studied in order to preserve a desired quality. The next sectiondescribes how to analyze these issues.

Problem Statement

Consider an image compression scenario between theoretical encoder anddecoder as an illustrative setting for describing primitive visualelement theory and dynamics. In an image set {I_(k)}_(k=1) ^(∞), eachmember takes values in a finite alphabet Λ(|Λ|)=256. Using a traditionalcompression system, I_(k) can be compressed into a code C_(k) by anencoding function ƒ:Λ″→{0,1}*, i.e. C_(k)=ƒ(I_(k)), where {0,1}*represents 0-1 sequences. On the decoder-side, a decoding functiong:{0,1}*→Λ″ is applied to present a reconstruction Ĩ_(k),Ĩ_(k)=g(C_(k)).Thus, a traditional compression processing function, which is composedof an encoder and a decoder, can be formulated asφ:I_(k)→Ĩ_(k)=φ(I_(k))(=g·ƒ(I_(k)). Then the encoding rate distortionoptimization is obtained as in Equation (1):

min(D(I _(k) ,Ĩ _(k))+λR _(k)),  (1)

where λ is a weighted factor, R_(k) is the length of C_(k) in bits andD(I,Ĩ_(k)) is the distortion between I and Ĩ_(k) determined by afidelity measure D.When some sort of knowledge is involved in compression, the encodingfunction is defined as in Equation (2):

where L( ) is a learning process, represents a type of primitiveelement, and Ω_(i) is one subset of image set {I}_(k=1) ^(∞) labeled byi. Correspondingly, the reconstructed image is obtained byĨ_(k)=g(C_(k)|L(ξ,Ω_(j))), where the function g is shown in Equation(3):

In typical learning-based coding schemes, the learned knowledge L(ξ,Ω)is required to be the same among decoders so that the decoders canprovide a unique reconstruction for an input image. Furthermore, thelearned knowledge should also be identical at both encoder and decoderto ensure correct decoding and equivalent quality as well.

In the exemplary system 100, as different training sets can be used, theserver side components constructs image Ĩ_(k) ^(i) as in Equation (4):

Ĩ _(k) ^(i) =g _(i)(C _(k) |L(ξ,Ω_(i)))  (4)

while the client-side components create a reconstruction Ĩ_(k) as inEquation (5):

Ĩ _(k) ^(j) =g _(j)(C _(k) |L(ξ,Ω_(j))).  (5)

The compression distortions at the encoder and decoder are D(I_(k),Ĩ_(k)^(i)) and D(I_(k),Ĩ_(k) ^(j)), respectively, measured by qualityassessment matrix D. Then, the rate-distortion optimization is obtainedas in Equation (6):

$\begin{matrix}{{\underset{t}{\arg \; \min}( {{\overset{\_}{D}( {I_{k},{\overset{\sim}{I}}_{k}^{t}} )} + {\lambda \; R_{k}}} )},} & (6)\end{matrix}$

where tε{i,j}. Accordingly, a target is to find a proper type ofprimitive elements ξ subject to Equation (6) to make the server-side andclient-side components have similar distortions though theirreconstructed images could be different in terms of pixel values.

Exemplary Selected Primitive Elements

In one implementation, as mentioned, primal sketch-based primitivepatches are used as the primitive visual elements in the exemplarysystem 100. Primal sketch, a known technique, can thus provide primitiveelements for the exemplary system 100. The primal sketch model is animportant contribution in computer vision, made first made by D. Marr,in Vision, W.H. Freeman and Company, 1982. The primal sketch modelconstitutes a symbolic or token representation of image intensityvariations and their local geometry. According to the definition ofprimal sketch given in the Marr reference, the process of generating aprimal sketch involves the following two steps. First, a classicalvisual edge is extracted as the zero-crossing position of a Laplacian orGaussian-filtered image. Then the edge-segment descriptors, bars, andblobs are grouped into units, associated with properties such as length,width, brightness, and position in the image to form the primalsketches. Compared with an edge model, the primal sketch model refersnot only to the two-dimensional geometry of images but also to theintensity changes by relevant gray-level information across them. Itmakes the primal sketch model a rich representation of images.

Moreover, recent progress shows that primal sketches can be wellrepresented by examples, and the dimensionality of image primitives,such as primal sketch, is intrinsically very low. Thus, it is possibleto represent the primal sketches of natural images by a limited numberof examples. For example, it has been shown that primal sketches of animage can be learned from those of other generic images. Given alow-resolution image, a set of candidate high frequency primitives canbe selected from the trained data based on low frequency primitives toenhance the quality of the up-sampled version. Thus, in oneimplementation, the exemplary system 100 selects primal sketch-basedprimitive elements and includes a coding framework that degradesedge-related regions, to be later recovered by primal sketch-basedlearning.

Exemplary Primal Sketch-based Primitive Patch

Generally, during compression, an original image I(x) is locallyfiltered with a low-pass filter G_(L)(x) of unity integral, accompaniedwith quantization noise q(x). It can be modeled as in Equation (7):

Ĩ(x)=I(x)*G _(L)(x)+q(x).  (7)

The degraded information of signal I(x) during compression is thedifference between I(x) and Ĩ(x) which could be estimated as in Equation(8):

d=I(x)−Ĩ(x)≈I(x)*G _(H)(x)+q′(x)  (8)

where G_(H)(x) and q′(x) correspond to local high-pass filtering andquantization noise. This approximation, although theoretically notprecisely accurate, is yet practical. At high and medium quality levels,quantization noise has a relatively low effect on the difference signal:there is some similarity between a histogram of its compressed versionand that of its high-pass filtered version. Thus, the distortion causedby compression at high quality levels can be simulated as the highfrequency components of the original signal, despite quantization noise.

Furthermore, the distortion, especially large distortion, caused bycompression mainly focuses on high frequency regions of an image.Accordingly, compression tends to cause a considerable truncation ofhigh frequency energy in primal sketch regions along visual edges, whileintroducing relatively few effects in low frequency regions of theimage. As humans are more sensitive to high-contrast intensity changes,such a type of distortion would result in visible artifacts and thusdegrade the perceptual quality of the entire image.

So, it is useful to exploit the high frequency signal of primal sketchregions. FIG. 3 shows an example of primitive patch extraction 300. Inone implementation of the exemplary system 100, primitive elements areedge-centered N×N patches 302, referred to herein as “primitive patches”302. Edges 304 for generating the primitive patches are extracted byconvolving image signal I(x) with the derivative of a Gaussian functionΨ(x; σ,θ) at scale σ and orientation θ, as shown in Equation (9):

E(x)=I(x)*Ψ(x;σ;θ).  (9)

An edge point 306 is identified by finding the local maximum in themagnitude of the response. Then, as shown in FIG. 3, the primal sketchregions 308 are extracted along the edge points whose local maximum isrecognized as an edge. After high-pass filtering, the “patch” containingthe high frequency signal of a primal sketch region 308 is treated as aprimitive patch 302. Some examples of primitive patches of size 9×9pixels are also depicted in FIG. 3.

Exemplary Learning-based Patch Mapping

Building on the above analysis, exemplary learning-based mapping studiesthe high-frequency components both of original primal sketch regions 308and of their distorted versions. The idea is to build a genericrelationship between the original primitive patch 302 and its recoveredversion. Trained data that contain pairs of patches are obtained from aset of generic images.

FIG. 4 shows exemplary patch mapping 400. In FIG. 4, G_(H) is ahigh-pass filter 402. The variables i and j denote two different images.M_(i) 404 is an original primal sketch region 408 of image i, and {tildeover (M)}_(i) 406 is its distorted version. Given a distorted patch{tilde over (M)}_(j) 408 of input image j, the goal is to use thesimilarity between primitive patches and their distorted versions, i.e.,as derived from training images (e.g. M_(i)*G_(H) 410 and {tilde over(M)}_(i) *G _(H) G_(H) 412 of image i), to infer the missing highfrequency signal M_(j)*G_(H) 414 according to the undistorted patchM_(j) 410.

An important aspect of this patch mapping process 400 is the definitionof similarity. This similarity should be able to measure therelationship between primitive patch M_(i) 404 and its distorted version{tilde over (M)}_(i) 406 in an image i. Meanwhile, it is also necessaryto measure the relationship between primitive patches from differentimages, such as {tilde over (M)}_(i)*G_(H) 412 and {tilde over(M)}_(j)*G_(H) 416. The metric should be able to preserve recognizablefeatures between an original patch 404 and its distorted version 412 inone image, and at the same be able to be applied across patches ofdifferent images.

For image patches generally, it may be hard to find a proper metric. Butsince patch primitives in contour regions are of low dimensionality, itis possible to represent the possible primitive patches by an affordablenumber of examples and further create appropriate patch mapping.

Let N=M*G_(H) denote an original primitive patch, and N′ be its mostsimilar patch in terms of pixel value. The metric e(N)=∥N−N′|/|N| isused to evaluate the effectiveness of patch mapping 400. For a givenmatch error e, the hit rate h represents the percentage of test datawith match errors are less than e. Receiver Operating Characteristic(ROC) curve can be adopted to show the relationship between e and h. Ata given match error, a high hit rate indicates a good generalization ofthe training data, which indicates that the training data are of lowdimensionality.

Exemplary Training Engine (Primitive Patch Learning)

Based on the above analyses, learning-based patch mapping 400 is appliedto develop the relationships between primitive patches. FIG. 5 shows theexemplary trainer 210 (FIG. 2) for primitive patch learning, in greaterdetail. The illustrated implementation is only one exampleconfiguration, for descriptive purposes. Many other arrangements of thecomponents of an exemplary trainer 210 are possible within the scope ofthis described subject matter. Such an exemplary trainer 210 can beexecuted in hardware, software, or combinations of hardware, software,firmware, etc.

In FIG. 5, the dashed lines indicate a signal of an original trainingimage 203, while the solid lines indicate a signal of the distortedtraining image 510. Given an input training image 203, a distortionmodule 502 simulates the process of lossy compression. For example, inone implementation, a distortion module 502 includes a down-samplefilter 504 followed by an up-sample filter 506. Then, an edge detector508, such as an orientation energy-based edge detector, is applied tothe reconstructed distorted image 510.

According to the detected edge information, the primal patch extractor512 determines primal sketch regions 408 of both the distorted image(input from the high-pass filter 514) and the differential signal 516that represents the difference between the distorted image 510 and theoriginal training image 203. In this training, a distorted primitivepatch 522 and the differential primitive patch 518 at the same imageposition are treated as a primitive pair in the following process. Afterthe normalizer 520, each pair of primitive patches is categorized intoseveral categories, e.g., by an edge classifier, according to the edgetype and orientation of the distorted primitive patch 522, andcorrespondingly stored into the trained set 116. Subsequently, certainclustering techniques may be applied to shrink the size of the trainedset 116 to a desirable level.

Specifically, let and {tilde over (M)}_(i) and {tilde over (M)}_(j)denote the primitive patches 522 of high-pass filtered distorted imagesĨ_(i)*G_(H) and Ĩ_(j)*G_(H), σ_(i) and σ_(j) are the standard deviationswith respect to the luminance distributions in Ĩ_(i)*G_(H) andĨ_(j)*G_(H), respectively. At primal sketch regions 408, if a normalizedprimitive patch 522′ of a distorted image Ĩ_(i)*G_(H) is similar to anormalized primitive patch 522 of another distorted image Ĩ_(j)*G_(H),the relationship between the corresponding normalized original primitivepatches of image I_(i)*G_(H) and I_(j)*G_(H) can be learned well by theexemplary learning-based training method. In other words, if primitivepatch {tilde over (M)}_(i)/σ_(i) is similar to {tilde over(M)}_(j)/σ_(j), the decoder can deduce the primitive patch M_(j)/σ_(j)from M_(i)/σ_(i) by the mapping given in Equations (9) and (10), thelatter primitive patch M_(i)/σ_(i) being found in the trained data 116.

{tilde over (M)} _(i)/σ_(i)

{tilde over (M)} _(j)/σ_(j)  (9)

M _(i)/σ_(i)

M _(j)/σ_(j)  (10)

An advantage of the exemplary patch mapping 400 is that it provides avery natural means of specifying image transformations. Rather thanselecting between different filters, the exemplary patch mapping 400simply works by being supplied with an appropriate exemplar that candirectly index the trained set 116 or be used directly as a searchcriterion, without having to perform additional or in-betweenprocessing.

Codebook Compression

Referring back to FIG. 2, which depicts server-side components 200, thecompressor 212 can significantly reduce the file size of the codebook116 and then save bandwidth in delivery. Since the codebook 116 may havea unique data structure, a more suitable compression technique can becustomized for its compression than conventional tools such as ZIPcompression. The codebook 116 is mainly composed of a number of pairs ofthe low-frequency and high-frequency primitive patches 302, as shown inthe codebook 116 in FIG. 5.

These primitive patches 302 are grouped into a number of subclasses(e.g., 48 subclasses in one implementation) by an edge classifieraccording to the edge type and orientation of the distorted primitivepatch. Each subclass may be built as an artificial neural network (ANN)tree. The primitive patches 302 in each subclass present with somesimilarity, and the elements in each N×N patch also present with somespatial correlations. The similarity and correlations can be exploitedand utilized by the compressor 212. In particular, in oneimplementation, predictive coding is used to exploit the spatialcorrelations within a patch, and context-based arithmetic coding is usedto exploit the correlations among patches.

Taking a 9×9 primitive patch 302 as an example, it should be noted thatthe original element is stored as a 4-byte floating-point value. To makethis easier to compress, the compressor 212 converts the 4-bytefloating-point value to an integer value by scaling and quantization.This operation may lose precision, but can achieve the trade-off betweencompression ratio and quality in reconstruction.

FIG. 6( a) depicts an illustrative 9×9 patch. The compressor 212compresses its elements in the raster scan order. For the compression ofthe current element “A,” the compressor 212 first obtains its prediction“refA” from either its “up” neighbor “uA” or its left neighbor “1A.” Forthe first element in the patch, the compressor 212 sets its prediction“refA” as a constant. For the element in the first row (excluding thefirst element), the prediction is from its left neighbor 1A. Similarly,for the element in the first column (excluding the first element), theprediction is from its up neighbor uA. For the element in the otherpositions, the prediction is calculated with the schema shown in FIG. 6(b).

After obtaining the prediction, the compressor 212 calculates theresidue of the current element by subtracting the prediction value. Theresidue is composed of a sign and magnitude. The sign is directlycompressed with the arithmetic coder, and the magnitude is compressedwith a context-based arithmetic coder. The patches in the same categorypresent some similarity, because they are grouped according to theiredge types and orientation of energies. There are two ways to define thecontexts to utilize this property. The first method is to extract theprobability distribution model of the current element according to itsco-located elements in previous patches. Accordingly, one implementationdefines 9×9=81 contexts in the arithmetic coder, and each contextcorresponds to the probability distribution model of residues at acertain position in the patch.

The second method is to extract the probability distribution modelaccording to its neighbor elements. In one implementation, thecompressor 212 takes the “up” and “left” neighbors as an example. Inparticular, the magnitude is quantized to M bits (e.g., 4 bits). Then,the compressor 212 defines 2M×2M (e.g., 24×24=256) contexts in thearithmetic coder. This magnitude is taken as zero if the neighbor doesnot exist. The compressor 212 can also combine the two methods to definecontexts. However, the number of contexts becomes too large to be usedin practice.

Exemplary Server-Side Components and Methods

In the exemplary image resizing system 100, learning-based imageenlarging on the client-side enriches the user experience of previewingthumbnail images, without downloading the original versions. FIG. 7depicts an application scenario. When the user moves the mouse pointeronto a thumbnail image 204 of interest (a “mouseover”), a browser withthe exemplary image resizing plug-in 122 may change the thumbnail 204into an enlarged image 704 for better preview. The plug-in 122facilitates the learning-based image resizing, employing complexitycontrol and quality control.

FIG. 8 depicts an exemplary image resizing architecture 800. The imageresizing can be performed online, upon a request to the server. First,the thumbnail image 204 is up-sampled to a larger version 704 throughthe bi-cubic interpolator 802. Other interpolation approaches such asbilinear interpolation may also been employed. The quality enhancer 804can then perform optional image quality enhancement to improve thevisual quality. For example, when the thumbnail 204 has significantblock artifacts due to JPEG compression, then de-blocking filtering canbe applied to remove these artifacts.

The hallucination engine 806 then further refines the enlarged image704, applying the codebook 116. It should be noted that the updatingengine 808 can maintain the codebook 116 offline with respect to theimage resizing functionality. If the server has an incremental codebookupdate available, then the client 114 can download this update offlineto reshape the codebook 116.

Considering the computing resources used and/or the delay in thumbnailpreviewing, the complexity control modality of the “complexity andquality” controller 810 can perform in harmony with the hallucinationengine 806.

To guarantee the highest degree of quality enhancement, quality controlcan also be applied. In general, the image enhancement starts from theimage regions with the largest distortions, and thereby highest visualquality can be achieved by accomplishing the most dramatic change first,given the available computing resources and/or predefined displaydelays.

Exemplary Image Hallucination

In one implementation, the exemplary hallucination engine 806 constructsa high-frequency version of the image from the high-frequency primitivesin the codebook 116 by using patches in the low-frequency image to indexcorresponding low-frequency primitives in the codebook 116. Thelow-frequency primitives in the codebook 116 are paired withhigh-frequency primitives used to make the high-frequency version of theimage. The constructed high-frequency image is then blended with thelow-frequency image to enhance its quality. The exemplary image resizingsystem 100 employs the method in Jian Sun, Nan-Ning Zheng, Hai Tao,Heung-Yeung Shum, “Image Hallucination with Primal Sketch Priors,”Proceedings of the 2003 IEEE Conference on Computer Vision and PatternRecognition, Jun. 16-22, 2003, as an example. Moreover, some operationsare improved to provide quality control scalability and complexitycontrol scalability.

FIG. 9 shows an implementation of the hallucination engine 806 ingreater detail. The interpolated image 902, that is, the low-frequencyinterpolated image 902, e.g., after quality enhancement, serves as aninput. In particular, the primitive patch extractor 512′ aims to enhancewith high-frequency information at primal sketch regions. Thus the edgedetector 508′ is applied on the low-frequency image to find these primalsketch regions. The primitive patches 302 are then extracted by the samemethod used in codebook training, as shown back in FIG. 5. For eachextracted primitive patch 302, the primitive patch extractor 512′searches the trained codebook 116 to find a potential candidate using anartificial neural network (ANN) method. The image blender 904 utilizesan averaging operator for pixel values in overlapped regions. Fornon-primal sketch regions, in one implementation the up-sampled valuesare used directly.

Exemplary Quality and Complexity Control

The quality of the small-size thumbnail 204 has a large impact on thequality of the image 704 after resizing. Typically, the thumbnail 204 isusually compressed by JPEG compression, for example, in real web-basedapplications. The undesirable JPEG compression artifacts may beamplified in the high-resolution image. In one implementation of theexemplary image resizing system 100, how to reduce these artifacts canbecome a major problem of quality control.

Referring back to FIG. 8, in one implementation, the complexity andquality controller 810 conducts quality control at various stages of theimage hallucination. For example, de-blocking (i.e. smoothing acrossblock boundaries) can first be applied to the interpolated image 902before the edge detector 508′ operates in order to avoid extractingedges along the block boundaries that should not be visible there. Theartifact level in a block is related to the quantization parameter (QP)of JPEG compression. “QP” can be used to indicate the artifact level,and is already available in image hallucination. The artifact level isthen used to control the strength of the smoothing across blockboundaries. In particular, the complexity and quality controller 810applies strong filtering on those blocks with a high artifact level, butextracts the high-frequency signals associated with edges from thecodebook 116 during operation of the hallucination engine 806.

Since the hallucination of the high-frequency image is mainly performedon the edge regions, the number of edges determines the overalloperations of the hallucination engine 806. That is to say, the numberof edges has a large impact on the complexity. Therefore, the complexitymodality of the complexity and quality controller 810 first applies thecomplexity control during operation of the edge detector 508′. Moreover,an early termination process can be applied to the ANN search within theprimitive patches matching, if computing resources are not amplyavailable.

Exemplary Method

FIG. 10 shows an exemplary method 1000 of image resizing at a remoteclient without downloading the resized image. In the flow diagram, theoperations are summarized in individual blocks. The exemplary method1000 may be performed by combinations of hardware, software, firmware,etc., for example, by components of the exemplary image resizing system100.

At block 1002, a codebook of primitive visual elements is created from acollection of training images. In one implementation, the codebook isgenerated from training images collected from popular web searches. Astatistical treatment is applied to web search logs to find the mostpopular image searches. Then, the codebook may be kept relevant andcurrent by sending incremental updates based on newly popular imagesearches.

To find primitive visual elements for the codebook within a trainingimage, the method 1000 can include detecting visual edges in eachtraining image, and finding edge-centered patches of each image withinimage regions known as primal sketch regions. Primitive patches areextracted from the primal sketch regions, and alow-frequency/low-quality version of each primitive patch is paired witha high-frequency/high-quality version of the primitive patch for thecodebook. The codebook thus consists of low and high-frequency primitivepatch pairs.

These primitive patch pairs provide fundamental primitive visualelements used as general purpose visual building blocks forhallucinating or synthesizing most images—or at least popular imageswith similarity to those from which the codebook was trained. Thelow-frequency member of each pair is used to index the entire pair. Thatis, a thumbnail image that has been enlarged through interpolationconstitutes a low-frequency version of the image. When the method 1000finds a patch to enhance in this low-frequency image, the method 1000tries to find a low-frequency primitive patch in the codebook thatmatches the low-frequency patch to be enhanced. When the method finds amatch, e.g., through an ANN search, then the high-frequency member ofthe found pair is used to refine the image at the location of thelow-frequency patch.

After its creation, the codebook is then sent to a user's browser, e.g.,over a background channel, and can be used to resize many images.

At block 1004, thumbnail-sized images are generated by an exemplarytechnique that combines conventional thumbnail generation withextraction of resizing parameters. The resizing parameters are of littledata size and can be stored with the thumbnail. Other metadata may alsobe derived at this point, during thumbnail production, such as flagsthat indicate which regions of each image can be hallucinated to higherquality later on during resizing, and/or flags that indicate whether animage is amenable to hallucination via the codebook at all.

At block 1006, when a client selects a thumbnail image in order to see alarger version of the thumbnail, the thumbnail is interpolated into alarger image via bi-cubic or bilinear interpolation. Although thethumbnail at this point in the method 1000 has been enlarged, it is oflow quality.

At block 1008, a high-quality version of the enlarged image is generatedin a manner that is similar to the process that was used to create thecodebook. That is, the method finds primal sketch regions in thelow-frequency enlarged image and extracts visual patches, such asedge-centered patches, to be used as index keys for searching thecodebook. When a low-frequency patch from the image matches alow-frequency member of a primitive patch pair in the codebook, thenthat pair is selected to enhance the patch in the image. Thehigh-frequency (or “good” quality) member of the primitive patch pair issubstituted as the primitive visual element for the image, at thelocation of the image patch being processed.

At block 1010, the high-frequency version of the image just created isnow blended with the enlarged image that was interpolated from thethumbnail, i.e., the low-frequency version of the image. This blendingcreates a reconstructed image that emulates the visual quality of theoriginal image from which the thumbnail was generated.

CONCLUSION

Although exemplary systems and methods have been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claimed methods, devices, systems, etc.

1. A method of resizing a thumbnail image to a larger image withoutdownloading the larger image, comprising: under control of one or moreprocessors configured with executable instructions: receiving athumbnail image and resizing parameters, the resizing parameterscomprising a plurality of parameters that were extracted when thethumbnail image was generated from an original image by downsampling;enlarging the thumbnail image to a low-frequency enlarged image;applying a codebook of primitive visual elements to parts of thelow-frequency enlarged image to hallucinate a high-frequency version ofthe enlarged image for emulating the quality of the original image,wherein the codebook of primitive visual elements includes a codebookthat is created from a collection of training images.
 2. The method asrecited in claim 1, further comprising blending the low-frequencyenlarged image with the high-frequency enlarged image to emulate animage quality level of the original image.
 3. The method as recited inclaim 1, further comprising applying the resizing parameters to a regionof the thumbnail image.
 4. The method as recited in claim 1, furthercomprising creating the codebook at a remote client or receiving, at theremote client, the codebook from a server.
 5. The method as recited inclaim 4, further comprising receiving, by a browser at the remoteclient, the codebook and an image resizing plug-in via a backgroundchannel.
 6. The method as recited in claim 1, wherein the codebook ofprimitive visual elements is trained from a collection of imagesassociated with statistically popular image search queries on theInternet.
 7. The method as recited in claim 1, further comprisingreceiving the codebook in a compressed format.
 8. The method as recitedin claim 1, further comprising receiving incremental updates of thecodebook, the incremental updates being obtained based on new trainingimages from newly popular image search queries on the Internet.
 9. Themethod as recited in claim 1, wherein enlarging the thumbnail imagecomprises up-sampling the thumbnail image via interpolation, theinterpolation comprising bi-cubic or bilinear interpolation.
 10. Themethod as recited in claim 1, wherein applying the codebook of primitivevisual elements comprises: finding high-frequency primitive visualelements in the codebook, the finding comprising: matching low-frequencyprimitive visual regions of the low-frequency enlarged image withcorresponding low-frequency primitive visual elements in the codebook;for each low-frequency primitive visual element found as a match in thecodebook, utilizing a corresponding high-frequency primitive visualelement paired with a respective low-frequency primitive visual elementin the codebook; and constructing high-frequency regions for thelow-frequency enlarged image using the high-frequency primitive visualelements in the codebook.
 11. The method as recited in claim 10, furthercomprising: applying edge detection on the low-frequency enlarged imageto find primal primitive regions of the low-frequency enlarged image;extracting primitive visual elements from the primal primitive regions;applying an artificial neural network (ANN) search method for matchingthe low-frequency primitive visual regions of the low-frequency enlargedimage with the corresponding low-frequency primitive visual elements inthe codebook; and for non-primal primitive regions in the low-frequencyenlarged image, directly using up-sampled image values from thethumbnail image.
 12. The method as recited in claim 11, furthercomprising matching a depth of the ANN search method to availablecomputing resources for complexity control or quality control.
 13. Themethod as recited in claim 1, further comprising applying an imagequality enhancement to the enlarged image prior to applying the codebookof primitive visual elements to the parts of the low-frequency enlargedimage.
 14. The method as recited in claim 13, wherein the image qualityenhancement comprises de-blocking filtering to remove JPEG compressionartifacts.
 15. The method as recited in claim 1, further comprisingapplying quality control scalability and complexity control scalabilityin resizing the thumbnail image.
 16. A system comprising: a processor;memory; a resizer stored in the memory and executable on the processorthat enlarges a thumbnail image via interpolation into an enlarged imageas directed by resizing parameters, the resizing parameters including aplurality of parameters that are extracted when the thumbnail image iscreated from an original image; a hallucinator stored in the memory andexecutable on the processor that synthesizes high-fidelity parts of theenlarged image by applying a codebook of primitive visual elements, thecodebook of primitive visual elements includes a codebook that iscreated based on a collected of training images and provides universalvisual parts for reconstructing images; and a blender stored in thememory and executable on the processor that combines the enlarged imagewith the high-fidelity parts to emulate the original image.
 17. Thesystem as recited in claim 16, wherein the hallucinatory is furtherconfigured to: find high-frequency primitive visual elements in thecodebook by: matching low-frequency primitive visual regions of theenlarged image with corresponding low-frequency primitive visualelements in the codebook; for each low-frequency primitive visualelement found as a match in the codebook, utilizing a high-frequencyprimitive visual element paired in the codebook with the low-frequencyprimitive visual element; and construct high-frequency regions of theimage using the high-frequency primitive visual elements in thecodebook.
 18. The system as recited in claim 17, further comprising: anedge detector that finds primitive visual regions of the enlarged imageassociated with visual edges; an extractor to derive primitive visualelements at the found primitive visual regions; and a mapper thatapplies an artificial neural network (ANN) search method for matchingthe derived primitive visual elements of the enlarged image withcorresponding primitive visual elements in the codebook, each primitivevisual element in the codebook being paired with a correspondinghigh-fidelity primitive visual element for reconstructing a high qualityversion of the original image.
 19. The system as recited in claim 16,further comprising an updater that refreshes the codebook with new pairsof primitive visual elements obtained from new training imagesassociated with new popular image search queries on the Internet.
 20. Amethod comprising: under control of one or more processors configuredwith executable instructions: creating a thumbnail image from anoriginal image; extracting resizing parameters for the thumbnail image;creating a codebook of primitive visual elements from a collection oftraining images, the primitive visual elements providing universalvisual parts for reconstructing images; transferring the thumbnail imageand the resizing parameters to a remote client, the resizing parametersenabling the remote client to resize the thumbnail image to an enlargedimage as directed by the resizing parameters; and transferring thecodebook of primitive visual elements to the remote client, the codebookof primitive visual elements enabling the remote client to hallucinate ahigh-quality version of the enlarged image, and blending the enlargedimage with the high-quality version of the image to emulate the originalimage.